Purpose - When firms' customers are located in spatially dispersed areas, it can be difficult to manage service quality on a geographically small scale because the relative importance of service quality might vary spatially.
Moreover, standard approaches discussed so far in the marketing science literature usually neglect spatial effects, such as spatial dependencies (spatial autocorrelation, for example) and spatial drift (spatial non-stationarity).
Design/methodology/approach - We propose a comprehensive but intelligible approach based on spatial econometric methods that covers spatial dependencies and spatial drift simultaneously. In particular, we incorporate the spatial expansion method (spatial drift) into spatial econometric models (spatial lag model, for example).
Findings - Using real company data on seasonal ticket revenue (dependent variable) and service quality (independent variables) of a regional public transport service provider, we find that the elasticity for the length of the public transport network is between 0.2 and 0.5, while the elasticity for the headway is between -0.2 and 0.6, for example. We control for several socio-economic, socio-demographic, and land-use variables.
Practical implications - Based on the empirical findings, we show that addressing spatial effects of service data can improve management's ability to implement programs aimed at enhancing seasonal ticket revenue. Therefore, we derive a spatial revenue response function that enables managers to identify small scale areas that are most efficient in terms of increasing revenue by service improvement.
Originality/value - The paper addresses the need to account for spatial effects in revenue response functions of public transport companies.
Keywords: Spatial econometrics; spatial expansion method; spatial dependencies; spatial drift; revenue response function; public transport seasonal ticket; revenue management; service quality
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